scholarly journals Multichannel matching pursuit for seismic trace decomposition

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. V61-V66 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
High Resolution ◽  
Correlation Coefficient ◽  
Frequency Spectrum ◽  
Matching Pursuit ◽  
Data Noise ◽  
Spatial Continuity ◽  
Time Frequency ◽  
Uniqueness Of The Solution ◽  
Single Trace

The technique of matching pursuit can adaptively decompose a seismic trace into a series of wavelets. However, the solution is not unique and is also strongly affected by data noise. Multichannel matching pursuit (MCMP), exploiting lateral coherence as a constraint, might improve the uniqueness of the solution. It extracts a constituent wavelet that has an optimal correlation coefficient to neighboring traces, instead of to a single trace only. According to linearity theory, a wavelet shared by neighboring traces is the best match to the average of multiple traces, and therefore it might effectively suppress the data noise and stabilize the performance. It is found that the MCMP scheme greatly improves spatial continuity in decomposition and can generate a plausible time-frequency spectrum with high resolution for reservoir detection.

scholarly journals Seismic time-frequency spectral decomposition by matching pursuit

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. V13-V20 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Frequency Spectrum ◽  
Acoustic Waves ◽  
Large Scale ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Gas Reservoir ◽  
Adaptive Wavelet ◽  
Redundant Dictionary ◽  
Time Frequency ◽  
Wavelet Selection

A seismic trace may be decomposed into a series of wavelets that match their time-frequency signature by using a matching pursuit algorithm, an iterative procedure of wavelet selection among a large and redundant dictionary. For reflection seismic signals, the Morlet wavelet may be employed, because it can represent quantitatively the energy attenuation and velocity dispersion of acoustic waves propagating through porous media. The efficiency of an adaptive wavelet selection is improved by making first a preliminary estimate and then a localized refining search, whereas complex-trace attributes and derived analytical expressions are also used in various stages. For a constituent wavelet, the scale is an important adaptive parameter that controls the width of wavelet in time and the bandwidth of the frequency spectrum. After matching pursuit decomposition, deleting wavelets with either very small or very large scale values can suppress spikes and sinusoid functions effectively from the time-frequency spectrum. This time-frequency spectrum may be used in turn for lithological analysis—for instance, detection of a gas reservoir. Investigation shows that the low-frequency shadow associated with a carbonate gas reservoir still exists, even high-frequency amplitudes are compensated by inverse-[Formula: see text] filtering.

scholarly journals High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

Sensors ◽  
2016 ◽  
Vol 16 (8) ◽  
pp. 1305 ◽  
Author(s):  
Tharoeun Thap ◽  
Heewon Chung ◽  
Changwon Jeong ◽  
Ki-Eun Hwang ◽  
Hak-Ryul Kim ◽  
...  
Keyword(s):  
High Resolution ◽  
Lung Function ◽  
Frequency Spectrum ◽  
Lung Function Test ◽  
Resolution Time ◽  
Time Frequency ◽  
Function Test

Detecting Karst Voids Based on Dominant Frequencies of Seismic Profiles

2021 ◽  
Author(s):  
Ying Rao ◽  
Yongxin Guo ◽  
Duo Xu
Keyword(s):  
High Resolution ◽  
Frequency Spectrum ◽  
High Speed ◽  
Dominant Frequency ◽  
Frequency Function ◽  
Resolution Time ◽  
High Speed Rail ◽  
Karst Terrain ◽  
Near Surface ◽  
Time Frequency

AbstractThe presence of near-surface karst voids is an extremely difficult issue in the construction of a high-speed rail (HSR) foundation. Seismic constant-offset profile (COP) method is one of the shallow geophysics technologies which may be used for the detection of karst voids. Although a COP image does not directly reveal the characters related to the anomalies in a karst terrain, the dominant frequency of the COP image in a karst terrain is significantly lower than the dominant frequency over the background without karstification or voids. This dominant-frequency anomaly is due to the strong attenuation effect when seismic waves propagate through any karst voids. Thus, we propose using the dominant-frequency anomalies of the COP image to directly detect near-surface karst voids in a karst terrain. First, we generate a high-resolution time–frequency spectrum for each COP trace, using the modified Wigner-Ville distribution (WVD) algorithm which combines WVD with a multichannel maximum entropy method. Second, we estimate a high-precision dominant-frequency function which varies along the reflection time, based on the corresponding high-resolution time–frequency spectrum. Finally, we detect the geological anomalies by analyzing low-frequency distributions in the dominant-frequency image for all traces. We demonstrate this procedure with a case study for the detection of karst voids within the high-speed rail foundation in a karst terrain, and verify the interpretation of hidden voids, cavities, clays and peats directly with drilling cores.

Matching pursuit-based sparse spectral analysis: Estimating frequency-dependent anomalies from nonstationary seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. V385-V396
Author(s):  
Jiao Xue ◽  
Chengguo Cai ◽  
Hanming Gu ◽  
Zongjie Li
Keyword(s):  
Spectral Analysis ◽  
Frequency Spectrum ◽  
Seismic Data ◽  
Fourier Spectrum ◽  
Matching Pursuit ◽  
Decomposition Methods ◽  
Seismic Wave Propagation ◽  
Frequency Dependent ◽  
Time Frequency ◽  
Convolution Model

Spectral decomposition has been widely used to detect frequency-dependent anomalies associated with hydrocarbons. By ignoring the time-variant feature of the frequency content of individual reflected wavelets, we have adopted a sparse time-frequency spectrum and developed a matching pursuit-based sparse spectral analysis (MP-SSA) method to estimate the sparse time-frequency representation of the seismic data. Further, we evaluate a generalized nonstationary convolution model concerning propagation attenuation and frequency-dependent reflectivity, and we mathematically evaluate the sparse time-frequency spectrum of the nonstationary seismic data as being equal to the product of the Fourier spectrum of the source wavelet, frequency-dependent reflection coefficient, and the cumulative attenuation during seismic wave propagation. Therefore, the reflectivity spectrum, which is a combination of the frequency-dependent reflectivity and the propagation attenuation, can be determined by dividing the sparse time-frequency spectrum of the seismic data by the Fourier spectrum of the source wavelet. Application of the matching pursuit-based decomposition methods to synthetic nonstationary convolutional data illustrates that the adopted MP-SSA spectrum shows a higher time resolution than the matching pursuit-based Wigner-Ville distribution and the matching pursuit-based instantaneous spectral analysis spectra. Notably, the MP-SSA method can avoid spectral smearing, which may introduce distortions to the frequency-dependent anomaly estimation. Application of the amplitude versus frequency analysis based on MP-SSA to field data illustrates the potential of using the sparse reflectivity spectral intercept and gradient to detect the hydrocarbon reservoirs.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

scholarly journals RANSAC algorithm for instantaneous frequency estimation and reconstruction of frequency-modulated undersampled signals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Igor Djurović
Keyword(s):  
Instantaneous Frequency ◽  
Matching Pursuit ◽  
Frequency Estimation ◽  
Consensus Algorithm ◽  
Multidimensional Space ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Polynomial Phase ◽  
Fm Signals ◽  
Fifth Order

AbstractFrequency modulated (FM) signals sampled below the Nyquist rate or with missing samples (nowadays part of wider compressive sensing (CS) framework) are considered. Recently proposed matching pursuit and greedy techniques are inefficient for signals with several phase parameters since they require a search over multidimensional space. An alternative is proposed here based on the random samples consensus algorithm (RANSAC) applied to the instantaneous frequency (IF) estimates obtained from the time-frequency (TF) representation of recordings (undersampled or signal with missing samples). The O’Shea refinement strategy is employed to refine results. The proposed technique is tested against third- and fifth-order polynomial phase signals (PPS) and also for signals corrupted by noise.

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